基于此,在预训练的时代,他通过总结之前的文章将这个过程构想为两个组件,第一个是neural knowledge retriever,用来建模p(z | x),其中x是当前的query,z是要找的辅助性知识;另一个是knowledge-augmented encoder,用来建模p(y | z, x),其中y是answer。 REALM的预训练和finetune 其中,retriver使用方法就是把query...
训练任务:检索和生成联合训练(unsupervised data) Prefix language modeling LM:一个由N个单词组成的块,并将这个块分成两个长度相等的子序列N/2。然后,输入第一个子序列,生成第二个子序列 检索:第一个子序列用作查询,第二个子序列对应于输出检索 Masked language modeling LM:15%的长度为3的token mask掉预测 检...
a) Prefix language modeling 以N个字符为单位将文本分块,将每个块的文本切分为长度为N/2的两段子序列,用第一段子序列作为query,通过检索模块召回相关的文档,然后去生成结果,生成的目标是对应的第二段子序列。 b) Masked language modeling 以N个字符为单位将文本分块,对于每一个分块,随机抽样若干个平均长度为3...
SUFFIX RETRIEVAL-AUGMENTED LANGUAGE MODELINGZecheng Wang and Yik-Cheung TamNYU ShanghaiDepartment of Computer Science567 West Yangsi Road, Pudong New District, Shanghai 200126, ChinaABSTRACTCausal language modeling (LM) uses word history to predict thenext word. BERT, on the other hand, makes use...
LLM之RAG:《Retrieval-Augmented Generation for Large Language Models: A Survey大型语言模型的检索增强生成研究综述》翻译与解读 导读:这篇论文主要围绕信息检索增强生成(Retrieval Augmented Generation,简称RAG)技术进行概述和分析。 背景痛点: >> 大语言模型(LLM)在处理知识密集型任务和回答离线知识更丰富的问题时面临...
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling Zile Qiao, Wei Ye, Yong Jiang, Tong Mo, Pengjun Xie, Weiping Li, Fei Huang, Shikun Zhang 2024 Self-Knowledge Guided Retrieval Augmentation for Large Language Models ...
内容提示: arXiv:2002.08909v1 [cs.CL] 10 Feb 2020REALM: Retrieval-Augmented Language Model Pre-TrainingKelvin Guu * 1 Kenton Lee * 1 Zora Tung 1 Panupong Pasupat 1 Ming-Wei Chang 1AbstractLanguage model pre-training has been shown tocapture a surprising amount of world knowledge,crucial ...
using masked language modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents. We demonstrate the effectiveness of Retrieval-Augmented Language Model pre-training (REALM) by fine-tuning on the challenging task of Open-domain Question Answering (...
We introduce REPLUG, a retrieval-augmented language modeling framework that treats the language model (LM) as a black box and augments it with a tuneable retrieval model. Unlike prior retrieval-augmented LMs that train language models with special cross attention mechanisms to encode the retrieved ...
To integrate knowledge in a more scalable and modular way, we propose a retrievalaugmented multimodal model, which enables a base multimodal model (generator) to refer to relevant knowledge fetched by a retriever from external memory (e.g., multimodal documents on the web). Specifically, we ...